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An Introduction to Time-series Analysis Using Python and Pandas
Author(s): Oscar Arzamendia Originally published on Towards AI. Assumptions Very recently I had the opportunity to work on building a sales forecaster as a POC. It was a challenging project with a cool MVP as an outcome, and through this post, I …
A Gaussian Approach to the Detection of Anomalous Behavior in Server Computers
Author(s): Navoneel Chakrabarty Originally published on Towards AI. Let’s detect the anomaly… Anomaly Detection is a different variant of Machine Learning Problems that falls under Semi-Supervised Learning. It is Semi-Supervised because, in Anomaly Detection (also popularly known as Outlier Detection), models often …
Application of Synthetic Minority Over-sampling Technique (SMOTe) for Imbalanced Datasets
Author(s): Navoneel Chakrabarty Originally published on Towards AI. In Data Science, imbalanced datasets are no surprises. If the datasets intended for classification problems like Sentiment Analysis, Medical Imaging or other problems related to Discrete Predictive Analytics (for example-Flight Delay Prediction) have an …
Bad and Good Regression Analysis
Author(s): Benjamin Obi Tayo Ph.D. Originally published on Towards AI. Regression models are the most popular machine learning models. Regression models are used for predicting target variables on a continuous scale. Regression models find applications in almost every field of study, and …
Machine Learning: Python Linear Regression Estimator Using Gradient Descent
Author(s): Benjamin Obi Tayo Ph.D. Originally published on Towards AI. Implementation Using Python Estimator In this article, we describe how a simple python estimator can be built to perform linear regression using the gradient descent method. Let’s assume we have a one-dimensional …
Machine Learning: Dimensionality Reduction via Linear Discriminant Analysis
Author(s): Benjamin Obi Tayo Ph.D. Originally published on Towards AI. A machine learning algorithm (such as classification, clustering or regression) uses a training dataset to determine weight factors that can be applied to unseen data for predictive purposes. Before implementing a machine …
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